Pages 71-96 | Published online: 15 Apr 2008
https://doi.org/10.1080/01969727108545830
Proto-organisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also randomly constructed molecular automata is examined by modeling the gene as a binary (on-off) device and studying the behavior of large, randomly constructed nets of these binary “genes.” The results suggest that, if each “gene” is directly affected by two or three other “genes,” then such random nets: behave with great order and stability; undergo behavior cycles whose length predicts cell replication time as a function of the number of genes per cell; possess different modes of behavior whose number per net predicts roughly the number of cell types in an organism as a function of its number of genes; and under the stimulus of noise are capable of differentiating directly from any mode of behavior to at most a few other modes of behavior. Cellular differentiation is modeled as a Markov chain among the modes of behavior of a genetic net. The possibility of a general theory of metabolic behavior is suggested. Analytic approaches to the behavior of switching nets are discussed in Appendix 1, and some implications of the results for the origin of self replicating macromolecular systems is discussed in Appendix 6.

ᔥ Michael Marshall in He may have found the key to the origins of life. So why have so few heard of him? (12/23/2020 09:03:11)

Read He may have found the key to the origins of life. So why have so few heard of him? by Michael Marshall (Science)

Hungarian biologist Tibor Gánti is an obscure figure. Now, more than a decade after his death, his ideas about how life began are finally coming to fruition.

Good to see Tibor Gánti finally getting some credit. This is a great little article with a nice overview of the Origin of Life problem (and references). The author Michael Marshall has a new book out on the topic.

ᔥ Peter Molnar in IndieWeb Chat (2020-12-22 12:01:21)

The ergodic hypothesis is a key analytical device of equilibrium statistical mechanics. It underlies the assumption that the time average and the expectation value of an observable are the same. Where it is valid, dynamical descriptions can often be replaced with much simpler probabilistic ones — time is essentially eliminated from the models. The conditions for validity are restrictive, even more so for non-equilibrium systems. Economics typically deals with systems far from equilibrium — specifically with models of growth. It may therefore come as a surprise to learn that the prevailing formulations of economic theory — expected utility theory and its descendants — make an indiscriminate assumption of ergodicity. This is largely because foundational concepts to do with risk and randomness originated in seventeenth-century economics, predating by some 200 years the concept of ergodicity, which arose in nineteenth-century physics. In this Perspective, I argue that by carefully addressing the question of ergodicity, many puzzles besetting the current economic formalism are resolved in a natural and empirically testable way.

↬ Kevin Marks retweet (2020-12-15) of 
↬ Simon Wardley @swardley in Simon Wardley on Twitter: “Anyway, this is a fabulous paper – The ergodicity problem in economics – … well worth the read.” / Twitter (2020-12-15 04:12)

From the New York Times-bestselling author of How Not to Be Wrong, himself a world-class geometer, a far-ranging exploration of the power of geometry, which turns out to help us think better about practically everything
How should a democracy choose its representatives? How can you stop a pandemic from sweeping the world? How do computers learn to play chess, and why is learning chess so much easier for them than learning to read a sentence? Can ancient Greek proportions predict the stock market? (Sorry, no.) What should your kids learn in school if they really want to learn to think? All these are questions about geometry.
For real. If you're like most people, geometry is a sterile and dimly-remembered exercise you gladly left behind in the dust of 9th grade, along with your braces and active romantic interest in pop singers. If you recall any of it, it's plodding through a series of miniscule steps, only to prove some fact about triangles that was obvious to you in the first place. That's not geometry. OK, it is geometry, but only a tiny part, a border section that has as much to do with geometry in all its flush modern richness as conjugating a verb has to do with a great novel.
Shape reveals the geometry underneath some of the most important scientific, political, and philosophical problems we face. Geometry asks: where are things? Which things are near each other? How can you get from one thing to another thing? Those are important questions. The word geometry, from the Greek, has the rather grand meaning of measuring the world. If anything, that's an undersell. Geometry doesn't just measure the world - it explains it. Shape shows us how.

If you want to transfer a few hundred gigabytes of data, it’s generally faster to FedEx a hard drive than to send the files over the internet. This isn’t a new idea—it’s often dubbed SneakerNet—and it’s how Google transfers large amounts of data internally.

But will it always be faster?

Cisco estimates that total internet traffic currently averages 167 terabits per second. FedEx has a fleet of 654 aircraft with a lift capacity of 26.5 million pounds daily. A solid-state laptop drive weighs about 78 grams and can hold up to a terabyte.

That means FedEx is capable of transferring 150 exabytes of data per day, or 14 petabits per second—almost a hundred times the current throughput of the internet.

Read Passphrases That You Can Memorize — But That Even the NSA Can’t Guess by Micah Lee (The Intercept)

IT’S GETTING EASIER to secure your digital privacy. iPhones now encrypt a great deal of personal information; hard drives on Mac and Windows 8.1 computers are now automatically locked down; even Facebook, which made a fortune on open sharing, is providing end-to-end encryption in the chat tool WhatsApp. But none of this technology offers as much protection as you may think if you don’t know how to come up with a good passphrase.

To recognize strange extraterrestrial life and solve biological mysteries on this planet, scientists are searching for an objective definition for life’s basic units.

I’ve been following a bit of David’s work, but obviously there’s some newer material I need to catch up on. I like the general philosophical thrust of their direction here. I can see some useful abstractions to higher math here, maybe an analogy to a “calculus of biology” which doesn’t look at single points, but rates of change of that point(s).

The Claude Shannon movie, "The Bit Player", is available for free on Vimeo this weekend. It's worth watching.https://t.co/myXK8vBVOC

— Peter Shor (@PeterShor1) June 27, 2020

This is a video I've been wanting to do for a while (in part because I've wanted to learn Morse Code myself, for years!) and I've also had many requests for it.

The first method is also useful for letter frequencies (or playing something like Wheel of Fortune) while the second is actually useful for the sound memory needed to practice Morse code.

Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work.

Different quantities that go by the name of entropy are used in variational principles to infer probability distributions from limited data. Shore and Johnson showed that maximizing the Boltzmann-Gibbs form of the entropy ensures that probability distributions inferred satisfy the multiplication rule of probability for independent events in the absence of data coupling such events. Other types of entropies that violate the Shore and Johnson axioms, including nonadditive entropies such as the Tsallis entropy, violate this basic consistency requirement. Here we use the axiomatic framework of Shore and Johnson to show how such nonadditive entropy functions generate biases in probability distributions that are not warranted by the underlying data.

Zero-width characters are invisible, ‘non-printing’ characters that are not displayed by the majority of applications. F​or exam​ple, I’ve ins​erted 10 ze​ro-width spa​ces in​to thi​s sentence, c​an you tel​​l? (Hint: paste the sentence into Diff Checker to see the locations of the characters!). These characters can be used to ‘fingerprint’ text for certain users.

A cool little trick with text for embedded steganography, security, or other communication purposes. 

This could also be used for pseudo-private communication via Webmention even. Just hide your messages inside of public messages.

ᔥ Aaron Parecki bookmark Be careful what you copy: Invisibly inserting usernames into text with Zero-Width Characters (medium.com) (2018-04-05)

ITBio – Chris Aldrich (feed) ❧

Hey, wait! He’s not only following me, but a very distinct subset of my posts!

This is the first time I’ve ever seen someone indicate that they’ve done this in the wild.

I’ll also admit that this is a really great looking blogroll too! I’m going to have to mine it for the bunch of feeds that I’m not already following. 

The media's "epistemic crisis," algorithmic biases, and the radio's inherent, historical misogyny.

In hearings this week, House Democrats sought to highlight an emerging set of facts concerning the President’s conduct. On this week’s On the Media, a look at why muddying the waters remains a viable strategy for Trump’s defenders. Plus, even the technology we trust for its clarity isn’t entirely objective, especially the algorithms that drive decisions in public and private institutions. And, how early radio engineers designed broadcast equipment to favor male voices and make women sound "shrill."

1. David Roberts [@drvox], writer covering energy for Vox, on the "epistemic crisis" at the heart of our bifurcated information ecosystem. Listen.

2. Cathy O'Neil [@mathbabedotorg], mathematician and author of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, on the biases baked into our algorithms. Listen.

3. Tina Tallon [@ttallon], musician and professor, on how biases built into radio technology have shaped how we hear women speak. Listen.

Some great discussion on the idea of women being “shrill” and ad hominem attacks instead of attacks on ideas.

Cathy O’Neil has a great interview on her book Weapons of Math Distraction. I highly recommend everyone read it, but if for some reason you can’t do it this month, this interview is a good starting place for repairing that deficiency.

In section three, I’ll note that I’ve studied the areas of signal processing and information theory in great depth, but never run across the fascinating history of how we physically and consciously engineered women out of radio and broadcast in quite the way discussed here. I recall the image of “Lena” being nudged out of image processing recently, but the engineering wrongs here are far more serious and pernicious.

President and William H. Miller Professor of Complex Systems

Listened to Mindscape 72 | César Hidalgo on Information in Societies, Economies, and the Universe by Sean Carroll from preposterousuniverse.com

Maxwell’s Demon is a famous thought experiment in which a mischievous imp uses knowledge of the velocities of gas molecules in a box to decrease the entropy of the gas, which could then be used to do useful work such as pushing a piston. This is a classic example of converting information (what the gas molecules are doing) into work. But of course that kind of phenomenon is much more widespread — it happens any time a company or organization hires someone in order to take advantage of their know-how. César Hidalgo has become an expert in this relationship between information and work, both at the level of physics and how it bubbles up into economies and societies. Looking at the world through the lens of information brings new insights into how we learn things, how economies are structured, and how novel uses of data will transform how we live.

César Hidalgo received his Ph.D. in physics from the University of Notre Dame. He currently holds an ANITI Chair at the University of Toulouse, an Honorary Professorship at the University of Manchester, and a Visiting Professorship at Harvard’s School of Engineering and Applied Sciences. From 2010 to 2019, he led MIT’s Collective Learning group. He is the author of Why Information Grows and co-author of The Atlas of Economic Complexity. He is a co-founder of Datawheel, a data visualization company whose products include the Observatory of Economic Complexity.

It was interesting to hear Cesar Hidalgo use the concept of “big history” a few times in this episode. I’m not 100% sure he meant it in the David Christian sense of the words, but it at least felt right.

I was also piqued at the mention of Lynne Kelly’s work, which I’m now knee deep into. I suspect it could dramatically expand on what we think of as the capacity of a personbyte, though the limit of knowledge there still exists. The idea of mnemotechniques within indigenous cultures certainly expands on the way knowledge worked in prehistory and what we classically think of and frame collective knowledge or collective learning.

I also think there are some interesting connections with Dr. Kelly’s mentions of social equity in prehistorical cultures and the work that Hidalgo mentions in the middle of the episode.

There are a small handful of references I’ll want to delve into after hearing this, though it may take time to pull them up unless they’re linked in the show notes.

hat-tip: Complexity Digest for the reminder that this is in my podcatcher. November 22, 2019 at 03:28PM

Read Humane Ingenuity 9: GPT-2 and You by Dan Cohen (buttondown.email)

This newsletter has not been written by a GPT-2 text generator, but you can now find a lot of artificially created text that has been.

For those not familiar with GPT-2, it is, according to its creators OpenAI (a socially conscious artificial intelligence lab overseen by a nonprofit entity), “a large-scale unsupervised language model which generates coherent paragraphs of text.” Think of it as a computer that has consumed so much text that it’s very good at figuring out which words are likely to follow other words, and when strung together, these words create fairly coherent sentences and paragraphs that are plausible continuations of any initial (or “seed”) text.

This isn’t a very difficult problem and the underpinnings of it are well laid out by John R. Pierce in *[An Introduction to Information Theory: Symbols, Signals and Noise]( In it he has a lot of interesting tidbits about language and structure from an engineering perspective including the reason why crossword puzzles work.
November 13, 2019 at 08:33AM

The most interesting examples have been the weird ones (cf. HI7), where the language model has been trained on narrower, more colorful sets of texts, and then sparked with creative prompts. Archaeologist Shawn Graham, who is working on a book I’d like to preorder right now, An Enchantment of Digital Archaeology: Raising the Dead with Agent Based Models, Archaeogaming, and Artificial Intelligence, fed GPT-2 the works of the English Egyptologist Flinders Petrie (1853-1942) and then resurrected him at the command line for a conversation about his work. Robin Sloan had similar good fun this summer with a focus on fantasy quests, and helpfully documented how he did it.

Circle back around and read this when it comes out.

Similarly, these other references should be an interesting read as well.
November 13, 2019 at 08:36AM

From this perspective, GPT-2 says less about artificial intelligence and more about how human intelligence is constantly looking for, and accepting of, stereotypical narrative genres, and how our mind always wants to make sense of any text it encounters, no matter how odd. Reflecting on that process can be the source of helpful self-awareness—about our past and present views and inclinations—and also, some significant enjoyment as our minds spin stories well beyond the thrown-together words on a page or screen.

And it’s not just happening with text, but it also happens with speech as I’ve written before: Complexity isn’t a Vice: 10 Word Answers and Doubletalk in Election 2016 In fact, in this mentioned case, looking at transcripts actually helps to reveal that the emperor had no clothes because there’s so much missing from the speech that the text doesn’t have enough space to fill in the gaps the way the live speech did.
November 13, 2019 at 08:43AM

This demo enables forensic inspection of the visual footprint of a language model on input text to detect whether a text could be real or fake.

The data and virtual unwrapping results on the En-Gedi scroll. 

 

See the following papers for more information:

Seales, William Brent, et al. "From damage to discovery via virtual unwrapping: Reading the scroll from En-Gedi." Science advances 2.9 (2016): e1601247. (Web Article)

 

Segal, Michael, et al. "An Early Leviticus Scroll From En-Gedi: Preliminary Publication." Textus 26 (2016): 1-30. (PDF)

The written word has been used throughout history to chronicle and contemplate the human experience, but many valuable texts are “lost” to us due to damage. The words of these documents and the knowledge they seek to impart are locked behind the destruction and decay wrought by time and injury, while the physical manuscripts themselves form an “invisible library” of sorts — closeted away on dark shelves, well-protected but prevented from proffering knowledge and encouraging inquiry. For more than 20 years, Dr. Seales has been working to create and use hi-tech, non-invasive tools to rescue these lost texts from the blink of oblivion and restore them to humanity. We call this innovative process “virtual unwrapping.”

h/t Dan Cohen newsletter #1

The eruption of Mt. Vesuvius covered the city of Herculaneum in twenty meters of lava, simultaneously destroying the Herculaneum scrolls through carbonization and preserving the scrolls by protecting them from the elements. Unwrapping the scrolls would damage them, but researchers are anxious to read the texts. Researchers from the University of Kentucky collaborated with the Institut de France and SkyScan to digitally unwrap and preserve the scrolls. To learn more about the EDUCE project, go to http://cs.uky.edu/dri.

They haven’t finished the last mile, but having high resolution scans of the objects is great. I’m not sure why they’re handling these items manually when they could very likely be secured in better external casings and still imaged the same way.

A cat is alive, a sofa is not: that much we know. But a sofa is also part of life. Information theory tells us why

A nice little essay in my area, but I’m not sure there’s anything new in it for me. It is nice that they’re trying to break some of the problem down into smaller components before building it back up into something else. Reframing things can always be helpful. Here, in particular, they’re reframing the definitions of life and alive.

About the Course:

This course aims to push the field of Origins of Life research forward by bringing new and synthetic thinking to the question of how life emerged from an abiotic world.

This course begins by examining the chemical, geological, physical, and biological principles that give us insight into origins of life research. We look at the chemical and geological environment of early Earth from the perspective of likely environments for life to originate.

Taking a look at modern life we ask what it can tell us about the origin of life by winding the clock backwards. We explore what elements of modern life are absolutely essential for life, and ask what is arbitrary? We ponder how life arose from the huge chemical space and what this early 'living chemistry'may have looked like.

We examine phenomena, that may seem particularly life like, but are in fact likely to arise given physical dynamics alone. We analyze what physical concepts and laws bound the possibilities for life and its formation.

Insights gained from modern evolutionary theory will be applied to proto-life. Once life emerges, we consider how living systems impact the geosphere and evolve complexity. 

The study of Origins of Life is highly interdisciplinary - touching on concepts and principles from earth science, biology, chemistry, and physics.  With this we hope that the course can bring students interested in a broad range of fields to explore how life originated. 

The course will make use of basic algebra, chemistry, and biology but potentially difficult topics will be reviewed, and help is available in the course discussion forum and instructor email. There will be pointers to additional resources for those who want to dig deeper.

This course is Complexity Explorer's first Frontiers Course.  A Frontiers Course gives students a tour of an active interdisciplinary research area. The goals of a Frontiers Course are to share the excitement and uncertainty of a scientific area, inspire curiosity, and possibly draw new people into the research community who can help this research area take shape!

I’m totally in for this!

Hat tip for the reminder to:

A new online course on The Origin of Life is just starting at Complexity Explorer @ComplexExplorer, a program of the Santa Fe Institute @sfiscience. I’m enrolled!

— Sean Carroll (@seanmcarroll) June 14, 2019

Replied to a tweet by John Stewart (Twitter)

What are ways to engage students in conversation around “salvation through technology.” #domains19

One example I’ve heard: “No worries about our planet with plans for space exploration.”

— Keegan Long-Wheeler (@KeeganSLW) June 10, 2019

There's a long history of using tech and rhetoric around tech as a way out of Malthusian limits on resources. Tech as salvation has been a dominant way of thinking, but needs to be challenged/critiqued. #Domains19

— John Stewart (@JohnStewartPhD) June 10, 2019

I bookmarked a great post by Jim Luke (@econproph) a few weeks ago on scale and scope. I suspect that tech’s effect on education is heavily (if not permanently) scale-limited, but scope may be a better avenue going forward.

I also suspect that Cesar Hidalgo’s text Why Information Grows: The Evolution of Order, from Atoms to Economies may provide a strong clue with some details. To some extent I think we’ve generally reached the Shannon limit for how much information we can pour into a single brain. We now need to rely on distributed and parallel networking among people to proceed forward.

As it happens, he’d already done some work on coding theory—in the area of biology. The digital nature of DNA had been discovered by Jim Watson and Francis Crick in 1953, but it wasn’t yet clear just how sequences of the four possible base pairs encoded the 20 amino acids. In 1956, Max Delbrück—Jim Watson’s former postdoc advisor at Caltech—asked around at JPL if anyone could figure it out. Sol and two colleagues analyzed an idea of Francis Crick’s and came up with “comma-free codes” in which overlapping triples of base pairs could encode amino acids. The analysis showed that exactly 20 amino acids could be encoded this way. It seemed like an amazing explanation of what was seen—but unfortunately it isn’t how biology actually works (biology uses a more straightforward encoding, where some of the 64 possible triples just don’t represent anything).  ❧

I recall talking to Sol about this very thing when I sat in on a course he taught at USC on combinatorics. He gave me his paper on it and a few related issues as I was very interested at the time about the applications of information theory and biology.

I’m glad I managed to sit in on the class and still have the audio recordings and notes. While I can’t say that Newton taught me calculus, I can say I learned combinatorics from Golomb.

Read Solomon Golomb (1932–2016) by Stephen Wolfram (blog.stephenwolfram.com)

The Most-Used Mathematical Algorithm Idea in History

An octillion. A billion billion billion. That’s a fairly conservative estimate of the number of times a cellphone or other device somewhere in the world has generated a bit using a maximum-length linear-feedback shift register sequence. It’s probably the single most-used mathematical algorithm idea in history. And the main originator of this idea was Solomon Golomb, who died on May 1—and whom I knew for 35 years.

Solomon Golomb’s classic book Shift Register Sequences, published in 1967—based on his work in the 1950s—went out of print long ago. But its content lives on in pretty much every modern communications system. Read the specifications for 3G, LTE, Wi-Fi, Bluetooth, or for that matter GPS, and you’ll find mentions of polynomials that determine the shift register sequences these systems use to encode the data they send. Solomon Golomb is the person who figured out how to construct all these polynomials.

A fantastic and pretty comprehensive obit for Sol. He did miss out on more of Sol’s youth as well as his cross-town chess rivalry with Basil Gordon when they both lived in Baltimore, but before they lived across town from each other again in Los Angeles.

Many of the fantastical seeming stories here, as well as Sol’s personality read very true to me with respect to the man I knew for almost two decades.

in June 1955 he wrote his final report, “Sequences with Randomness Properties”—which would basically become the foundational document of the theory of shift register sequences.  ❧

Liked a tweet by Lior Pachter (Twitter)

I once asked Robert McEliece whether he would mentor me. I told him I liked combinatorics and it seemed relevant for EE. He explained that "you find an interesting problem and then use the relevant tools, not the other way around." Formative experience. https://t.co/3IDTgdSJsF

— Lior Pachter (@lpachter) May 13, 2019

Alumnus and engineering faculty member Robert J. McEliece has passed away.

May is apparently the month that many of the greats in information theory pass away. I was reminded of Sol Golomb’s passing in May 2016 the other day.

I didn’t know him well, but met Dr. McEliece a handful of times and at least a few of the books in my personal information theory library are hand-me-down copies from his personal library. He’ll definitely be missed.

The Bit Player Trailer from IEEE Information Theory Society on Vimeo.

In a blockbuster paper in 1948, Claude Shannon introduced the notion of a "bit" and laid the foundation for the information age. His ideas ripple through nearly every aspect of modern life, influencing such diverse fields as communication, computing, cryptography, neuroscience, artificial intelligence, cosmology, linguistics, and genetics. But when interviewed in the 1980s, Shannon was more interested in showing off the gadgets he’d constructed — juggling robots, a Rubik’s Cube solving machine, a wearable computer to win at roulette, a unicycle without pedals, a flame-throwing trumpet — than rehashing the past. Mixing contemporary interviews, archival film, animation and dialogue drawn from interviews conducted with Shannon himself, The Bit Player tells the story of an overlooked genius who revolutionized the world, but never lost his childlike curiosity.

Gallager gave a nice concise summary of what he learned from Shannon about how to do good theory work:

1. Simplify the problem
2. Relate it to other problems
3. Restate the problem in as many ways as possible
4. Break the problem into pieces
5. Avoid getting locked into thinking ruts
6. Generalize

As he said, “it’s a process of doing research… each one [step] gives you a little insight.” It’s tempting, as a theorist, to claim that at the end of this process you’ve solved the “fundamental” problem, but Gallager admonished us to remember that the first step is to simplify, often dramatically. As Alfred North Whitehead said, we should “seek simplicity and distrust it.”

I know I’ve read this before, but it deserves a re-read/review every now and then.

New research explains how the shapes of neurons can be classified using mathematical methods from the field of algebraic topology. Neuroscientists can now start building a formal catalogue for all the types of cells in the brain. Onto this catalogue of cells, they can systematically map the function and role in disease of each type of neuron in the brain.

Read Random passwords Not easy to see by Jeena (jeena.net)

Even if you look really close.

Background

Organisms live and die by the amount of information they acquire about their environment. The systems analysis of complex metabolic networks allows us to ask how such information translates into fitness. A metabolic network transforms nutrients into biomass. The better it uses information on available nutrient availability, the faster it will allow a cell to divide.

Results

I here use metabolic flux balance analysis to show that the accuracy I (in bits) with which a yeast cell can sense a limiting nutrient's availability relates logarithmically to fitness as indicated by biomass yield and cell division rate. For microbes like yeast, natural selection can resolve fitness differences of genetic variants smaller than 10-6, meaning that cells would need to estimate nutrient concentrations to very high accuracy (greater than 22 bits) to ensure optimal growth. I argue that such accuracies are not achievable in practice. Natural selection may thus face fundamental limitations in maximizing the information processing capacity of cells.

Conclusion

The analysis of metabolic networks opens a door to understanding cellular biology from a quantitative, information-theoretic perspective.

https://doi.org/10.1186/1752-0509-1-33

Received: 01 March 2007 Accepted: 30 July 2007 Published: 30 July 2007

Hat tip to Paul Davies in The Demon in the Machine

Self-replication is a capacity common to every species of living thing, and simple physical intuition dictates that such a process must invariably be fueled by the production of entropy. Here, we undertake to make this intuition rigorous and quantitative by deriving a lower bound for the amount of heat that is produced during a process of self-replication in a system coupled to a thermal bath. We find that the minimum value for the physically allowed rate of heat production is determined by the growth rate, internal entropy, and durability of the replicator, and we discuss the implications of this finding for bacterial cell division, as well as for the pre-biotic emergence of self-replicating nucleic acids.
https://doi.org/10.1063/1.4818538

Syndicated copy also available on arXiv:

Hat tip to Paul Davies in The Demon in the Machine

Bookmarked The notion of information in biology, an appraisal by Jérôme Segal (Journal BIO Web of Conferences Volume 4, Page 00017, 2015; ORIGINS – Studies in Biological and Cultural Evolution)

Developed during the first half of the 20th century, in three different fields, theoretical physics, statistics applied to agronomy and telecommunication engineering, the notion of information has become a scientific concept in the context of the Second War World. It is in this highly interdisciplinary environment that “information theory” emerged, combining the mathematical theory of communication and cybernetics. This theory has grown exponentially in many disciplines, including biology. The discovery of the genetic “code” has benefited from the development of a common language based on information theory and has fostered a almost imperialist development of molecular genetics, which culminated in the Human Genome Project. This project however could not fill all the raised expectations and epigenetics have shown the limits of this approach. Still, the theory of information continues to be applied in the current research, whether the application of the self-correcting coding theory to explain the conservation of genomes on a geological scale or aspects the theory of evolution.

[pdf]

https://doi.org/10.1051/bioconf/20150400017

The statistical definition of information is compared with Boltzmann's formula for entropy. The immediate result is that information I corresponds to a negative term in the total entropy S of a system.
S=S0−I
. A generalized second principle states that S must always increase. If an experiment yields an increase ΔI of the information concerning a physical system, it must be paid for by a larger increase ΔS0 in the entropy of the system and its surrounding laboratory. The efficiency ε of the experiment is defined as ε = ΔI/ΔS0≤1. Moreover, there is a lower limit k ln2 (k, Boltzmann's constant) for the ΔS0 required in an observation. Some specific examples are discussed: length or distance measurements, time measurements, observations under a microscope. In all cases it is found that higher accuracy always means lower efficiency. The information ΔI increases as the logarithm of the accuracy, while ΔS0 goes up faster than the accuracy itself. Exceptional circumstances arise when extremely small distances (of the order of nuclear dimensions) have to be measured, in which case the efficiency drops to exceedingly low values. This stupendous increase in the cost of observation is a new factor that should probably be included in the quantum theory.

https://doi.org/10.1063/1.1721463

First appearance of the word “negentropy” that I’ve seen in the literature.